A comprehensive evaluation of PID, cascade, model-predictive, and RTDA controllers for regulation of hypnosis

Abstract

Although manual control of anesthesia is still the dominant practice during surgery, an increasing number of studies have been conducted to investigate the possibility of automating this procedure. Several clinical studies that compare closed-loop to manual anesthesia control performance have been reported. These studies used proportional-integral-derivative (PID) controllers, as well as model-based controllers. However, there is a need for a comprehensive evaluation of closed-loop systems, to establish their safety, reliability, and efficacy for anesthesia regulation. This requires a detailed evaluation of promising and/or recent controllers for a range of patients and conditions via simulation. The present study investigates the performance of singleloop PID, cascade, model-predictive, and RTDA (Robustness, set point Tracking, Disturbance rejection, Aggressiveness) controllers for closed-loop regulation of hypnosis using isoflurane with bispectral index (BIS) as the controlled variable. Extensive simulations are performed using a model that simulates patient responses to the drug, surgical stimuli, and unexpected surgical events. Results of this comprehensive evaluation show that model-predictive and RTDA controllers provide better regulation of BIS, compared to the other controllers tested. ©2009 American Chemical Society.